Feat: Added temp param to model, created summary and scorer agents

core/model.py

@@ -5,7 +5,7 @@ from openai import OpenAI
 # Load environment variables from .env file
 load_dotenv()
 
-def generate_response(system_prompt, user_prompt):
+def generate_response(system_prompt: str, user_prompt: str, temp: float = 0.7):
     """
     Generate a response using Gemini LLM.
     
@@ -34,7 +34,8 @@ def generate_response(system_prompt, user_prompt):
         messages=[
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": user_prompt}
-        ]
+        ],
+        temperature=temp
     )
     
     # Return the generated response

core/response_evaluator.py

@@ -0,0 +1,169 @@
+from model import generate_response
+import json
+from typing import List
+from pydantic import BaseModel
+
+
+SYS_PROMPT= """
+You are Scorer API. You always respond in proper, directly parsable JSON.
+You are a highly accurate, impartial, JSON-only scoring system. 
+Your sole task is to evaluate two written answers to the same question: one from a user and one from a competitor.
+
+You are provided with the following input:
+- job_description: a role-specific job description that defines the expected skills, tone, and content quality.
+- question: an open-ended prompt relevant to the job.
+- user: the user's written answer to the question.
+- competitor: the competitor's written answer to the same question.
+
+Evaluate each answer based on the rubric and **how well it aligns with the job description**.
+
+Rubric criteria (each scored from 0-5):
+1. structure_star - Logical organization and coherence.
+2. depth - Insight, reasoning, and sophistication, especially in relation to the job requirements.
+3. clarity - How clear, readable, and accessible the response is.
+4. correctness - Factual and conceptual accuracy, including relevance to the job description.
+
+Important:
+- Improvement tip should be less than 25 words.
+Your output must strictly follow this **parsable JSON format**:
+
+{
+  "user": {
+    "structure_star": {"score": 0-5, "improvement_tip": "STRING"},
+    "depth": {"score": 0-5, "improvement_tip": "STRING"},
+    "clarity": {"score": 0-5, "improvement_tip": "STRING"},
+    "correctness": {"score": 0-5, "improvement_tip": "STRING"}
+  },
+  "competitor": {
+    "structure_star": {"score": 0-5, "improvement_tip": "STRING"},
+    "depth": {"score": 0-5, "improvement_tip": "STRING"},
+    "clarity": {"score": 0-5, "improvement_tip": "STRING"},
+    "correctness": {"score": 0-5, "improvement_tip": "STRING"}
+  }
+}
+
+Only output a valid JSON object. Do not include any commentary, headers, or extra text outside of the JSON.
+
+"""
+
+def trim_backticks(model_response: str):
+    return model_response[8:-4]
+    
+
+def scorer(jd:str, ques: str, user: str, competitor: str):
+    user_prompt = f"""
+        You are Scorer API.
+        Please evaluate the following answers based on the rubric criteria (structure_star, depth, clarity, correctness), considering the job description provided. 
+        Return a **valid, strictly formatted JSON object** as described.
+
+        job_description:
+        {jd}
+
+        question:
+        {ques}
+
+        user:
+        {user}
+
+        competitor:
+        {competitor}
+    """
+    response = generate_response(system_prompt=SYS_PROMPT, user_prompt=user_prompt, temp=0.1)
+    if response.startswith("```"):
+        response = trim_backticks(response)
+    
+    parsed_response = json.loads(response)
+    return parsed_response
+
+class Collect_score(BaseModel):
+    category: str
+    score: str
+    improvement_tip: str
+
+def improvement_summary(scores: List[Collect_score]):
+    system_prompt = """
+        You are Summarizer, a writing assistant focused on delivering concise improvement insights.
+
+        You will receive a list of objects, each containing:
+        - category (string): the evaluation dimension (e.g., "structure_star", "depth", etc.)
+        - score (string): a number between "0" and "5"
+        - improvement_tip (string): an actionable suggestion
+
+        Your task:
+        1. For these five, rewrite the `improvement_tip` into a short, readable, properly formatted string.
+        2. Each line should start with the category in bold, followed by a colon and the rewritten improvement tip.
+        3. Return a simple, plain text string of 5 lines. No extra text or formatting beyond what is specified.
+        4. Merge improvement tips of same categories.
+
+        Format example:
+        **structure_star**: Consider using clearer paragraph breaks to improve organization.  
+        **depth**: Expand on your examples to show deeper understanding.  
+        ...
+
+        """
+    scores_json = json.dumps([score.model_dump() for score in scores])
+    user_prompt = f"""
+        Here are the scores and improvement tips to summarize:
+        {scores_json}
+        
+        Please provide a concise summary with formatting as described.
+    """
+    response = generate_response(system_prompt=system_prompt, user_prompt=user_prompt, temp=0.3)
+    return response
+
+
+
+if __name__ == "__main__":
+    jd = """
+        We are seeking a product manager with experience in agile development, 
+        cross-functional collaboration, and data-driven decision-making. 
+        Strong communication skills and the ability to prioritize customer needs 
+        are essential.
+        """
+    ques = "How do you prioritize features during a product sprint?"
+    user = """
+        I look at customer pain points and align them with strategic goals. 
+        Then I negotiate with engineering based on effort and value.
+        """
+    competitor = """
+        I use a RICE scoring model and validate assumptions with customer interviews and 
+        analytics. Prioritization is then presented in sprint planning.
+        """
+    print(scorer(jd=jd,
+                 ques=ques,
+                 user=user,
+                 competitor=competitor))
+
+    scores = [
+        Collect_score(
+            category="structure_star",
+            score="3",
+            improvement_tip="Use more paragraph breaks and bullet points"
+        ),
+        Collect_score(
+            category="depth",
+            score="4",
+            improvement_tip="Include more specific industry examples"
+        ),
+        Collect_score(
+            category="clarity",
+            score="2", 
+            improvement_tip="Simplify technical jargon for broader audience"
+        ),
+        Collect_score(
+            category="correctness",
+            score="5",
+            improvement_tip="Excellent factual accuracy, maintain this standard"
+        ),
+        Collect_score(
+            category="depth",
+            score="3",
+            improvement_tip="include more in depth approach."
+        )
+    ]
+    summary = improvement_summary(scores)
+    print(summary)
+
+    
+    
+